The Internet of Things (IoT) enables communication of various devices, and IoT devices communicate with each other and collect a massive amount of data. Predicting network traffic greatly influences IoT networks resulting in reliable communication. In IoT, network Traffic Prediction allows traffic operators to take early actions to control the traffic load and improve the network performance. Existing learning models have successfully applied for IoT for network management improvement, avoiding congestion, and resource allocation optimization, but also for real-time or offloading data analyzing and decision making.
Deep learning-based traffic prediction accurately learns the patterns from a large amount of data and utilizes historical data to make better decisions than the other machine learning approaches, and also it promotes traffic predictions via powerful fair representation learning. Fast and accurate traffic prediction is an important technique to enable a solution for improving QoS and provides better efficiency. Thus, an adaptive design of the deep learning model significantly employs long-term traffic forecasting gives detailed predicting of traffic models to assess future capacity requirements and thus allows for more accurate planning and better decisions.